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- Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)

3.5

ISBN-13:
9780262029445

ISBN-10:
0262029448

Edition:
1

Author:
John D. Kelleher, Brian Mac Namee, Aoife DArcy
Publication date:
2015

Publisher:
The MIT Press

Format:
Hardcover
624 pages
Category:
Computers

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Book details

ISBN-13:
9780262029445

ISBN-10:
0262029448

Edition:
1

Author:
John D. Kelleher, Brian Mac Namee, Aoife DArcy
Publication date:
2015

Publisher:
The MIT Press

Format:
Hardcover
624 pages
Category:
Computers

Acknowledged authors
John D. Kelleher
,
Brian Mac Namee
,
Aoife DArcy
wrote Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)
comprising 624 pages back in 2015.
Textbook and eTextbook are published under ISBN 0262029448 and 9780262029445.
Since then Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press) textbook
received total rating of 3.5 stars and was available to sell back to BooksRun online for the top buyback price
of $ 24.79 or rent at the marketplace.

**A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.**

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.

After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.

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